Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations9709
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory184.0 B

Variable types

Numeric9
Categorical14

Alerts

Income_type is highly overall correlated with UnemployedHigh correlation
Occupation_type is highly overall correlated with UnemployedHigh correlation
Unemployed is highly overall correlated with Income_type and 1 other fieldsHigh correlation
term_deposit is highly imbalanced (78.2%)Imbalance
Education_type is highly imbalanced (51.5%)Imbalance
Housing_type is highly imbalanced (73.5%)Imbalance
ID has unique valuesUnique
tenure has 407 (4.2%) zerosZeros
balance has 3524 (36.3%) zerosZeros
Kids has 6819 (70.2%) zerosZeros
Years_employed has 1696 (17.5%) zerosZeros

Reproduction

Analysis started2024-10-07 13:11:20.194762
Analysis finished2024-10-07 13:12:02.715326
Duration42.52 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct9709
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5076104.7
Minimum5008804
Maximum5150479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:03.311123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5008804
5-th percentile5021595.2
Q15036955
median5069449
Q35112986
95-th percentile5143323.6
Maximum5150479
Range141675
Interquartile range (IQR)76031

Descriptive statistics

Standard deviation40802.696
Coefficient of variation (CV)0.0080381904
Kurtosis-1.2079678
Mean5076104.7
Median Absolute Deviation (MAD)35509
Skewness0.12677004
Sum4.92839 × 1010
Variance1.66486 × 109
MonotonicityNot monotonic
2024-10-07T14:12:03.885120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5008804 1
 
< 0.1%
5097146 1
 
< 0.1%
5097151 1
 
< 0.1%
5097154 1
 
< 0.1%
5097155 1
 
< 0.1%
5097157 1
 
< 0.1%
5097158 1
 
< 0.1%
5097163 1
 
< 0.1%
5097165 1
 
< 0.1%
5097176 1
 
< 0.1%
Other values (9699) 9699
99.9%
ValueCountFrequency (%)
5008804 1
< 0.1%
5008806 1
< 0.1%
5008808 1
< 0.1%
5008812 1
< 0.1%
5008815 1
< 0.1%
5008819 1
< 0.1%
5008825 1
< 0.1%
5008827 1
< 0.1%
5008830 1
< 0.1%
5008834 1
< 0.1%
ValueCountFrequency (%)
5150479 1
< 0.1%
5150467 1
< 0.1%
5150459 1
< 0.1%
5150451 1
< 0.1%
5150428 1
< 0.1%
5150410 1
< 0.1%
5150400 1
< 0.1%
5150388 1
< 0.1%
5150338 1
< 0.1%
5150337 1
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
0
7726 
1
1983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7726
79.6%
1 1983
 
20.4%

Length

2024-10-07T14:12:04.288199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:04.666199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7726
79.6%
1 1983
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7726
79.6%
1 1983
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7726
79.6%
1 1983
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7726
79.6%
1 1983
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7726
79.6%
1 1983
 
20.4%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
Male
5283 
Female
4426 

Length

Max length6
Median length4
Mean length4.9117314
Min length4

Characters and Unicode

Total characters47688
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5283
54.4%
Female 4426
45.6%

Length

2024-10-07T14:12:05.212201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:05.610293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 5283
54.4%
female 4426
45.6%

Most occurring characters

ValueCountFrequency (%)
e 14135
29.6%
a 9709
20.4%
l 9709
20.4%
M 5283
 
11.1%
F 4426
 
9.3%
m 4426
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37979
79.6%
Uppercase Letter 9709
 
20.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14135
37.2%
a 9709
25.6%
l 9709
25.6%
m 4426
 
11.7%
Uppercase Letter
ValueCountFrequency (%)
M 5283
54.4%
F 4426
45.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 47688
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14135
29.6%
a 9709
20.4%
l 9709
20.4%
M 5283
 
11.1%
F 4426
 
9.3%
m 4426
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14135
29.6%
a 9709
20.4%
l 9709
20.4%
M 5283
 
11.1%
F 4426
 
9.3%
m 4426
 
9.3%

age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.936348
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:05.910546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.495229
Coefficient of variation (CV)0.26954838
Kurtosis1.382193
Mean38.936348
Median Absolute Deviation (MAD)6
Skewness1.0104706
Sum378033
Variance110.14984
MonotonicityNot monotonic
2024-10-07T14:12:06.311555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 470
 
4.8%
35 464
 
4.8%
38 463
 
4.8%
36 437
 
4.5%
34 435
 
4.5%
33 428
 
4.4%
40 412
 
4.2%
32 408
 
4.2%
39 405
 
4.2%
31 395
 
4.1%
Other values (60) 5392
55.5%
ValueCountFrequency (%)
18 20
 
0.2%
19 27
 
0.3%
20 39
 
0.4%
21 50
 
0.5%
22 82
0.8%
23 96
1.0%
24 127
1.3%
25 150
1.5%
26 193
2.0%
27 202
2.1%
ValueCountFrequency (%)
92 2
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%
80 3
< 0.1%
79 4
< 0.1%
78 4
< 0.1%

term_deposit
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
0
9370 
1
 
339

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9370
96.5%
1 339
 
3.5%

Length

2024-10-07T14:12:06.683564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:06.933544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9370
96.5%
1 339
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 9370
96.5%
1 339
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9370
96.5%
1 339
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9370
96.5%
1 339
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9370
96.5%
1 339
 
3.5%

Car
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
0
6139 
1
3570 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 6139
63.2%
1 3570
36.8%

Length

2024-10-07T14:12:07.203564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:07.471565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6139
63.2%
1 3570
36.8%

Most occurring characters

ValueCountFrequency (%)
0 6139
63.2%
1 3570
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6139
63.2%
1 3570
36.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6139
63.2%
1 3570
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6139
63.2%
1 3570
36.8%

Property
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
1
6520 
0
3189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6520
67.2%
0 3189
32.8%

Length

2024-10-07T14:12:07.745569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:08.010974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6520
67.2%
0 3189
32.8%

Most occurring characters

ValueCountFrequency (%)
1 6520
67.2%
0 3189
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6520
67.2%
0 3189
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6520
67.2%
0 3189
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6520
67.2%
0 3189
32.8%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
Greece
4870 
Netherlands
2429 
Spain
2410 

Length

Max length11
Median length6
Mean length7.0026779
Min length5

Characters and Unicode

Total characters67989
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreece
2nd rowSpain
3rd rowGreece
4th rowGreece
5th rowSpain

Common Values

ValueCountFrequency (%)
Greece 4870
50.2%
Netherlands 2429
25.0%
Spain 2410
24.8%

Length

2024-10-07T14:12:08.306988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:08.636988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
greece 4870
50.2%
netherlands 2429
25.0%
spain 2410
24.8%

Most occurring characters

ValueCountFrequency (%)
e 19468
28.6%
r 7299
 
10.7%
G 4870
 
7.2%
c 4870
 
7.2%
a 4839
 
7.1%
n 4839
 
7.1%
N 2429
 
3.6%
t 2429
 
3.6%
h 2429
 
3.6%
l 2429
 
3.6%
Other values (5) 12088
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58280
85.7%
Uppercase Letter 9709
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19468
33.4%
r 7299
 
12.5%
c 4870
 
8.4%
a 4839
 
8.3%
n 4839
 
8.3%
t 2429
 
4.2%
h 2429
 
4.2%
l 2429
 
4.2%
d 2429
 
4.2%
s 2429
 
4.2%
Other values (2) 4820
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
G 4870
50.2%
N 2429
25.0%
S 2410
24.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 67989
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19468
28.6%
r 7299
 
10.7%
G 4870
 
7.2%
c 4870
 
7.2%
a 4839
 
7.1%
n 4839
 
7.1%
N 2429
 
3.6%
t 2429
 
3.6%
h 2429
 
3.6%
l 2429
 
3.6%
Other values (5) 12088
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 19468
28.6%
r 7299
 
10.7%
G 4870
 
7.2%
c 4870
 
7.2%
a 4839
 
7.1%
n 4839
 
7.1%
N 2429
 
3.6%
t 2429
 
3.6%
h 2429
 
3.6%
l 2429
 
3.6%
Other values (5) 12088
17.8%

cr_score
Real number (ℝ)

Distinct460
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.36914
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:08.963990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1583
median652
Q3717
95-th percentile813
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.74427
Coefficient of variation (CV)0.14875286
Kurtosis-0.41984632
Mean650.36914
Median Absolute Deviation (MAD)67
Skewness-0.069411833
Sum6314434
Variance9359.4537
MonotonicityNot monotonic
2024-10-07T14:12:09.511971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 231
 
2.4%
678 58
 
0.6%
655 53
 
0.5%
667 50
 
0.5%
705 50
 
0.5%
684 50
 
0.5%
670 49
 
0.5%
651 49
 
0.5%
663 47
 
0.5%
683 47
 
0.5%
Other values (450) 9025
93.0%
ValueCountFrequency (%)
350 5
0.1%
351 1
 
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
 
< 0.1%
382 1
 
< 0.1%
ValueCountFrequency (%)
850 231
2.4%
849 8
 
0.1%
848 5
 
0.1%
847 6
 
0.1%
846 5
 
0.1%
845 6
 
0.1%
844 7
 
0.1%
843 2
 
< 0.1%
842 7
 
0.1%
841 11
 
0.1%

tenure
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0080338
Minimum0
Maximum10
Zeros407
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:09.847992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.890235
Coefficient of variation (CV)0.57711971
Kurtosis-1.163669
Mean5.0080338
Median Absolute Deviation (MAD)2
Skewness0.0097615522
Sum48623
Variance8.3534583
MonotonicityNot monotonic
2024-10-07T14:12:10.138321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1022
10.5%
1 999
10.3%
7 997
10.3%
8 993
10.2%
5 986
10.2%
3 977
10.1%
9 958
9.9%
4 957
9.9%
6 946
9.7%
10 467
4.8%
ValueCountFrequency (%)
0 407
 
4.2%
1 999
10.3%
2 1022
10.5%
3 977
10.1%
4 957
9.9%
5 986
10.2%
6 946
9.7%
7 997
10.3%
8 993
10.2%
9 958
9.9%
ValueCountFrequency (%)
10 467
4.8%
9 958
9.9%
8 993
10.2%
7 997
10.3%
6 946
9.7%
5 986
10.2%
4 957
9.9%
3 977
10.1%
2 1022
10.5%
1 999
10.3%

balance
Real number (ℝ)

ZEROS 

Distinct6185
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76300.816
Minimum0
Maximum250898.09
Zeros3524
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:10.474305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97001.36
Q3127616.56
95-th percentile162618.71
Maximum250898.09
Range250898.09
Interquartile range (IQR)127616.56

Descriptive statistics

Standard deviation62403.395
Coefficient of variation (CV)0.81786013
Kurtosis-1.4929338
Mean76300.816
Median Absolute Deviation (MAD)47160.94
Skewness-0.13706484
Sum7.4080462 × 108
Variance3.8941837 × 109
MonotonicityNot monotonic
2024-10-07T14:12:10.873688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3524
36.3%
130170.82 2
 
< 0.1%
115576.44 1
 
< 0.1%
139626.01 1
 
< 0.1%
115761.51 1
 
< 0.1%
116085.06 1
 
< 0.1%
63832.43 1
 
< 0.1%
100895.54 1
 
< 0.1%
81818.49 1
 
< 0.1%
127510.99 1
 
< 0.1%
Other values (6175) 6175
63.6%
ValueCountFrequency (%)
0 3524
36.3%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 1
< 0.1%
238387.56 1
< 0.1%
222267.63 1
< 0.1%
221532.8 1
< 0.1%
216109.88 1
< 0.1%
214346.96 1
< 0.1%
213146.2 1
< 0.1%
212778.2 1
< 0.1%
212696.32 1
< 0.1%
212692.97 1
< 0.1%

Unemployed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
0
8013 
1
1696 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 8013
82.5%
1 1696
 
17.5%

Length

2024-10-07T14:12:11.768694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:12.005694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8013
82.5%
1 1696
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 8013
82.5%
1 1696
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8013
82.5%
1 1696
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8013
82.5%
1 1696
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8013
82.5%
1 1696
 
17.5%

Kids
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42280358
Minimum0
Maximum19
Zeros6819
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:12.234716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76701891
Coefficient of variation (CV)1.8141259
Kurtosis48.027416
Mean0.42280358
Median Absolute Deviation (MAD)0
Skewness3.6185747
Sum4105
Variance0.58831801
MonotonicityNot monotonic
2024-10-07T14:12:12.609697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 6819
70.2%
1 1886
 
19.4%
2 852
 
8.8%
3 126
 
1.3%
4 18
 
0.2%
5 5
 
0.1%
14 1
 
< 0.1%
19 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 6819
70.2%
1 1886
 
19.4%
2 852
 
8.8%
3 126
 
1.3%
4 18
 
0.2%
5 5
 
0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 1
 
< 0.1%
7 1
 
< 0.1%
5 5
 
0.1%
4 18
 
0.2%
3 126
 
1.3%
2 852
 
8.8%
1 1886
 
19.4%
0 6819
70.2%

Acc_length
Real number (ℝ)

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.270059
Minimum0
Maximum60
Zeros57
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:13.012722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q113
median26
Q341
95-th percentile56
Maximum60
Range60
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.648057
Coefficient of variation (CV)0.61048849
Kurtosis-1.0899609
Mean27.270059
Median Absolute Deviation (MAD)14
Skewness0.21630047
Sum264765
Variance277.1578
MonotonicityNot monotonic
2024-10-07T14:12:13.402716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 219
 
2.3%
13 216
 
2.2%
7 215
 
2.2%
16 212
 
2.2%
5 211
 
2.2%
15 210
 
2.2%
18 206
 
2.1%
39 201
 
2.1%
6 197
 
2.0%
3 196
 
2.0%
Other values (51) 7626
78.5%
ValueCountFrequency (%)
0 57
 
0.6%
1 136
1.4%
2 167
1.7%
3 196
2.0%
4 183
1.9%
5 211
2.2%
6 197
2.0%
7 215
2.2%
8 189
1.9%
9 185
1.9%
ValueCountFrequency (%)
60 100
1.0%
59 98
1.0%
58 112
1.2%
57 90
0.9%
56 114
1.2%
55 101
1.0%
54 102
1.1%
53 115
1.2%
52 104
1.1%
51 136
1.4%

Total_income
Real number (ℝ)

Distinct263
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181228.19
Minimum27000
Maximum1575000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:13.800716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum27000
5-th percentile67500
Q1112500
median157500
Q3225000
95-th percentile360000
Maximum1575000
Range1548000
Interquartile range (IQR)112500

Descriptive statistics

Standard deviation99277.305
Coefficient of variation (CV)0.54780276
Kurtosis15.780787
Mean181228.19
Median Absolute Deviation (MAD)45000
Skewness2.6593626
Sum1.7595445 × 109
Variance9.8559833 × 109
MonotonicityNot monotonic
2024-10-07T14:12:14.231716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135000 1139
 
11.7%
180000 845
 
8.7%
112500 843
 
8.7%
157500 829
 
8.5%
225000 750
 
7.7%
202500 558
 
5.7%
90000 535
 
5.5%
270000 426
 
4.4%
67500 265
 
2.7%
315000 232
 
2.4%
Other values (253) 3287
33.9%
ValueCountFrequency (%)
27000 2
< 0.1%
29250 1
 
< 0.1%
30150 1
 
< 0.1%
31500 3
< 0.1%
31531.5 1
 
< 0.1%
31950 1
 
< 0.1%
32400 1
 
< 0.1%
33300 2
< 0.1%
33750 1
 
< 0.1%
36000 4
< 0.1%
ValueCountFrequency (%)
1575000 1
 
< 0.1%
1350000 1
 
< 0.1%
1125000 3
 
< 0.1%
990000 1
 
< 0.1%
945000 1
 
< 0.1%
900000 10
0.1%
810000 6
0.1%
787500 1
 
< 0.1%
765000 2
 
< 0.1%
742500 1
 
< 0.1%

credit_card
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
1
6841 
0
2868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6841
70.5%
0 2868
29.5%

Length

2024-10-07T14:12:14.601716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:14.858697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6841
70.5%
0 2868
29.5%

Most occurring characters

ValueCountFrequency (%)
1 6841
70.5%
0 2868
29.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6841
70.5%
0 2868
29.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6841
70.5%
0 2868
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6841
70.5%
0 2868
29.5%

Years_employed
Real number (ℝ)

ZEROS 

Distinct3637
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6647302
Minimum0
Maximum43.020733
Zeros1696
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size76.0 KiB
2024-10-07T14:12:15.174836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.92815048
median3.7618842
Q38.2000315
95-th percentile18.792994
Maximum43.020733
Range43.020733
Interquartile range (IQR)7.271881

Descriptive statistics

Standard deviation6.3422413
Coefficient of variation (CV)1.1196017
Kurtosis4.2228184
Mean5.6647302
Median Absolute Deviation (MAD)3.2717989
Skewness1.8449651
Sum54998.865
Variance40.224024
MonotonicityNot monotonic
2024-10-07T14:12:15.548835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1696
 
17.5%
1.09790071 17
 
0.2%
0.547581401 17
 
0.2%
0.344976283 14
 
0.1%
2.798140961 14
 
0.1%
0.681738845 13
 
0.1%
1.839873509 13
 
0.1%
0.679000938 13
 
0.1%
1.259437223 13
 
0.1%
4.213638884 12
 
0.1%
Other values (3627) 7887
81.2%
ValueCountFrequency (%)
0 1696
17.5%
0.046544419 1
 
< 0.1%
0.117730001 1
 
< 0.1%
0.177963955 1
 
< 0.1%
0.180701862 1
 
< 0.1%
0.19165349 2
 
< 0.1%
0.194391397 1
 
< 0.1%
0.199867212 3
 
< 0.1%
0.213556747 1
 
< 0.1%
0.216294654 1
 
< 0.1%
ValueCountFrequency (%)
43.0207328 1
< 0.1%
42.87836164 1
< 0.1%
41.69011 1
< 0.1%
41.26573441 1
< 0.1%
41.17264557 1
< 0.1%
40.75922161 1
< 0.1%
40.54840277 1
< 0.1%
40.45257603 1
< 0.1%
39.79821625 1
< 0.1%
39.62572811 1
< 0.1%

Income_type
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
Working
4960 
Commercial associate
2312 
Pensioner
1712 
State servant
722 
Student
 
3

Length

Max length20
Median length7
Mean length10.894531
Min length7

Characters and Unicode

Total characters105775
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorking
2nd rowWorking
3rd rowCommercial associate
4th rowPensioner
5th rowWorking

Common Values

ValueCountFrequency (%)
Working 4960
51.1%
Commercial associate 2312
23.8%
Pensioner 1712
 
17.6%
State servant 722
 
7.4%
Student 3
 
< 0.1%

Length

2024-10-07T14:12:15.911860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:16.212837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
working 4960
38.9%
commercial 2312
18.1%
associate 2312
18.1%
pensioner 1712
 
13.4%
state 722
 
5.7%
servant 722
 
5.7%
student 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 11296
10.7%
i 11296
10.7%
r 9706
 
9.2%
e 9495
 
9.0%
n 9109
 
8.6%
a 8380
 
7.9%
s 7058
 
6.7%
W 4960
 
4.7%
k 4960
 
4.7%
g 4960
 
4.7%
Other values (11) 24555
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 93032
88.0%
Uppercase Letter 9709
 
9.2%
Space Separator 3034
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11296
12.1%
i 11296
12.1%
r 9706
10.4%
e 9495
10.2%
n 9109
9.8%
a 8380
9.0%
s 7058
7.6%
k 4960
5.3%
g 4960
5.3%
c 4624
 
5.0%
Other values (6) 12148
13.1%
Uppercase Letter
ValueCountFrequency (%)
W 4960
51.1%
C 2312
23.8%
P 1712
 
17.6%
S 725
 
7.5%
Space Separator
ValueCountFrequency (%)
3034
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102741
97.1%
Common 3034
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11296
11.0%
i 11296
11.0%
r 9706
9.4%
e 9495
9.2%
n 9109
8.9%
a 8380
 
8.2%
s 7058
 
6.9%
W 4960
 
4.8%
k 4960
 
4.8%
g 4960
 
4.8%
Other values (10) 21521
20.9%
Common
ValueCountFrequency (%)
3034
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 11296
10.7%
i 11296
10.7%
r 9706
 
9.2%
e 9495
 
9.0%
n 9109
 
8.6%
a 8380
 
7.9%
s 7058
 
6.7%
W 4960
 
4.7%
k 4960
 
4.7%
g 4960
 
4.7%
Other values (11) 24555
23.2%

Education_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
Secondary / secondary special
6761 
Higher education
2457 
Incomplete higher
 
371
Lower secondary
 
114
Academic degree
 
6

Length

Max length29
Median length29
Mean length25.078587
Min length15

Characters and Unicode

Total characters243488
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigher education
2nd rowSecondary / secondary special
3rd rowSecondary / secondary special
4th rowHigher education
5th rowHigher education

Common Values

ValueCountFrequency (%)
Secondary / secondary special 6761
69.6%
Higher education 2457
 
25.3%
Incomplete higher 371
 
3.8%
Lower secondary 114
 
1.2%
Academic degree 6
 
0.1%

Length

2024-10-07T14:12:16.564856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:16.875840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
secondary 13636
41.4%
6761
20.5%
special 6761
20.5%
higher 2828
 
8.6%
education 2457
 
7.5%
incomplete 371
 
1.1%
lower 114
 
0.3%
academic 6
 
< 0.1%
degree 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 26562
10.9%
c 23237
9.5%
23231
9.5%
a 22860
9.4%
r 16584
 
6.8%
o 16578
 
6.8%
n 16464
 
6.8%
d 16105
 
6.6%
y 13636
 
5.6%
s 13636
 
5.6%
Other values (15) 54595
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 203787
83.7%
Space Separator 23231
 
9.5%
Uppercase Letter 9709
 
4.0%
Other Punctuation 6761
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26562
13.0%
c 23237
11.4%
a 22860
11.2%
r 16584
8.1%
o 16578
8.1%
n 16464
8.1%
d 16105
7.9%
y 13636
6.7%
s 13636
6.7%
i 12052
5.9%
Other values (8) 26073
12.8%
Uppercase Letter
ValueCountFrequency (%)
S 6761
69.6%
H 2457
 
25.3%
I 371
 
3.8%
L 114
 
1.2%
A 6
 
0.1%
Space Separator
ValueCountFrequency (%)
23231
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 6761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 213496
87.7%
Common 29992
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26562
12.4%
c 23237
10.9%
a 22860
10.7%
r 16584
7.8%
o 16578
7.8%
n 16464
7.7%
d 16105
7.5%
y 13636
 
6.4%
s 13636
 
6.4%
i 12052
 
5.6%
Other values (13) 35782
16.8%
Common
ValueCountFrequency (%)
23231
77.5%
/ 6761
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26562
10.9%
c 23237
9.5%
23231
9.5%
a 22860
9.4%
r 16584
 
6.8%
o 16578
 
6.8%
n 16464
 
6.8%
d 16105
 
6.6%
y 13636
 
5.6%
s 13636
 
5.6%
Other values (15) 54595
22.4%

Family_status
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
Married
6530 
Single / not married
1359 
Civil marriage
836 
Separated
 
574
Widow
 
410

Length

Max length20
Median length7
Mean length9.4561747
Min length5

Characters and Unicode

Total characters91810
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCivil marriage
2nd rowMarried
3rd rowSingle / not married
4th rowSeparated
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 6530
67.3%
Single / not married 1359
 
14.0%
Civil marriage 836
 
8.6%
Separated 574
 
5.9%
Widow 410
 
4.2%

Length

2024-10-07T14:12:17.289835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:17.649836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
married 7889
54.0%
single 1359
 
9.3%
1359
 
9.3%
not 1359
 
9.3%
civil 836
 
5.7%
marriage 836
 
5.7%
separated 574
 
3.9%
widow 410
 
2.8%

Most occurring characters

ValueCountFrequency (%)
r 18024
19.6%
i 12166
13.3%
e 11232
12.2%
a 10709
11.7%
d 8873
9.7%
M 6530
 
7.1%
4913
 
5.4%
n 2718
 
3.0%
g 2195
 
2.4%
l 2195
 
2.4%
Other values (10) 12255
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75829
82.6%
Uppercase Letter 9709
 
10.6%
Space Separator 4913
 
5.4%
Other Punctuation 1359
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 18024
23.8%
i 12166
16.0%
e 11232
14.8%
a 10709
14.1%
d 8873
11.7%
n 2718
 
3.6%
g 2195
 
2.9%
l 2195
 
2.9%
m 2195
 
2.9%
t 1933
 
2.5%
Other values (4) 3589
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
M 6530
67.3%
S 1933
 
19.9%
C 836
 
8.6%
W 410
 
4.2%
Space Separator
ValueCountFrequency (%)
4913
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85538
93.2%
Common 6272
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 18024
21.1%
i 12166
14.2%
e 11232
13.1%
a 10709
12.5%
d 8873
10.4%
M 6530
 
7.6%
n 2718
 
3.2%
g 2195
 
2.6%
l 2195
 
2.6%
m 2195
 
2.6%
Other values (8) 8701
10.2%
Common
ValueCountFrequency (%)
4913
78.3%
/ 1359
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 18024
19.6%
i 12166
13.3%
e 11232
12.2%
a 10709
11.7%
d 8873
9.7%
M 6530
 
7.1%
4913
 
5.4%
n 2718
 
3.0%
g 2195
 
2.4%
l 2195
 
2.4%
Other values (10) 12255
13.3%

Housing_type
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
House / apartment
8684 
With parents
 
448
Municipal apartment
 
323
Rented apartment
 
144
Office apartment
 
76

Length

Max length19
Median length17
Mean length16.806159
Min length12

Characters and Unicode

Total characters163171
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRented apartment
2nd rowHouse / apartment
3rd rowHouse / apartment
4th rowHouse / apartment
5th rowHouse / apartment

Common Values

ValueCountFrequency (%)
House / apartment 8684
89.4%
With parents 448
 
4.6%
Municipal apartment 323
 
3.3%
Rented apartment 144
 
1.5%
Office apartment 76
 
0.8%
Co-op apartment 34
 
0.4%

Length

2024-10-07T14:12:18.085838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:18.489837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
apartment 9261
33.0%
house 8684
30.9%
8684
30.9%
with 448
 
1.6%
parents 448
 
1.6%
municipal 323
 
1.1%
rented 144
 
0.5%
office 76
 
0.3%
co-op 34
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 19562
12.0%
a 19293
11.8%
e 18757
11.5%
18393
11.3%
n 10176
 
6.2%
p 10066
 
6.2%
r 9709
 
6.0%
m 9261
 
5.7%
s 9132
 
5.6%
u 9007
 
5.5%
Other values (15) 29815
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126351
77.4%
Space Separator 18393
 
11.3%
Uppercase Letter 9709
 
6.0%
Other Punctuation 8684
 
5.3%
Dash Punctuation 34
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 19562
15.5%
a 19293
15.3%
e 18757
14.8%
n 10176
8.1%
p 10066
8.0%
r 9709
7.7%
m 9261
7.3%
s 9132
7.2%
u 9007
7.1%
o 8752
6.9%
Other values (6) 2636
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
H 8684
89.4%
W 448
 
4.6%
M 323
 
3.3%
R 144
 
1.5%
O 76
 
0.8%
C 34
 
0.4%
Space Separator
ValueCountFrequency (%)
18393
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 8684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136060
83.4%
Common 27111
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 19562
14.4%
a 19293
14.2%
e 18757
13.8%
n 10176
7.5%
p 10066
7.4%
r 9709
7.1%
m 9261
6.8%
s 9132
6.7%
u 9007
6.6%
o 8752
6.4%
Other values (12) 12345
9.1%
Common
ValueCountFrequency (%)
18393
67.8%
/ 8684
32.0%
- 34
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 19562
12.0%
a 19293
11.8%
e 18757
11.5%
18393
11.3%
n 10176
 
6.2%
p 10066
 
6.2%
r 9709
 
6.0%
m 9261
 
5.7%
s 9132
 
5.6%
u 9007
 
5.5%
Other values (15) 29815
18.3%

Occupation_type
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
Other
2994 
Laborers
1724 
Sales staff
959 
Core staff
877 
Managers
782 
Other values (14)
2373 

Length

Max length21
Median length20
Mean length8.7819549
Min length5

Characters and Unicode

Total characters85264
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowSecurity staff
3rd rowSales staff
4th rowOther
5th rowAccountants

Common Values

ValueCountFrequency (%)
Other 2994
30.8%
Laborers 1724
17.8%
Sales staff 959
 
9.9%
Core staff 877
 
9.0%
Managers 782
 
8.1%
Drivers 623
 
6.4%
High skill tech staff 357
 
3.7%
Accountants 300
 
3.1%
Medicine staff 291
 
3.0%
Cooking staff 193
 
2.0%
Other values (9) 609
 
6.3%

Length

2024-10-07T14:12:18.978836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
staff 3171
23.1%
other 2994
21.8%
laborers 1777
12.9%
sales 959
 
7.0%
core 877
 
6.4%
managers 782
 
5.7%
drivers 623
 
4.5%
high 357
 
2.6%
skill 357
 
2.6%
tech 357
 
2.6%
Other values (14) 1495
10.9%

Most occurring characters

ValueCountFrequency (%)
r 9979
11.7%
e 9787
11.5%
s 8210
 
9.6%
a 8161
 
9.6%
t 7508
 
8.8%
f 6342
 
7.4%
4040
 
4.7%
h 3708
 
4.3%
o 3393
 
4.0%
O 2994
 
3.5%
Other values (27) 21142
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71329
83.7%
Uppercase Letter 9802
 
11.5%
Space Separator 4040
 
4.7%
Dash Punctuation 53
 
0.1%
Other Punctuation 40
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9979
14.0%
e 9787
13.7%
s 8210
11.5%
a 8161
11.4%
t 7508
10.5%
f 6342
8.9%
h 3708
 
5.2%
o 3393
 
4.8%
i 2751
 
3.9%
n 2214
 
3.1%
Other values (11) 9276
13.0%
Uppercase Letter
ValueCountFrequency (%)
O 2994
30.5%
L 1830
18.7%
C 1216
12.4%
S 1187
 
12.1%
M 1073
 
10.9%
D 623
 
6.4%
H 379
 
3.9%
A 300
 
3.1%
P 86
 
0.9%
W 40
 
0.4%
Other values (3) 74
 
0.8%
Space Separator
ValueCountFrequency (%)
4040
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81131
95.2%
Common 4133
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9979
12.3%
e 9787
12.1%
s 8210
10.1%
a 8161
10.1%
t 7508
9.3%
f 6342
 
7.8%
h 3708
 
4.6%
o 3393
 
4.2%
O 2994
 
3.7%
i 2751
 
3.4%
Other values (24) 18298
22.6%
Common
ValueCountFrequency (%)
4040
97.7%
- 53
 
1.3%
/ 40
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9979
11.7%
e 9787
11.5%
s 8210
 
9.6%
a 8161
 
9.6%
t 7508
 
8.8%
f 6342
 
7.4%
4040
 
4.7%
h 3708
 
4.3%
o 3393
 
4.0%
O 2994
 
3.5%
Other values (27) 21142
24.8%

Target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.0 KiB
0
8426 
1
1283 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9709
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8426
86.8%
1 1283
 
13.2%

Length

2024-10-07T14:12:19.428842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-07T14:12:19.779839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8426
86.8%
1 1283
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 8426
86.8%
1 1283
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9709
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8426
86.8%
1 1283
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9709
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8426
86.8%
1 1283
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8426
86.8%
1 1283
 
13.2%

Interactions

2024-10-07T14:11:52.640395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:27.636415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:30.594664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:33.830666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:37.506666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:40.413185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:43.291189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:46.380408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:49.600694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:52.956132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:27.992029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:30.938684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:34.270665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:37.954668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:40.720182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:43.613208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:46.740406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:49.953695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:53.265070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:28.342008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:31.297665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:34.835673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:38.286667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:41.052203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:43.923184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:47.056426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:50.297015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:53.547711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:28.650011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:31.594688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:35.104668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:38.583668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:41.370203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:44.237187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:47.358404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:50.606001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:53.866710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:28.946013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:31.919662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:35.404665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:38.866676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:41.659183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:44.662192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:47.657427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:50.921584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:54.190708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:29.260943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:32.234683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:35.765666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:39.170688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:41.950184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:44.953620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:48.302405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:51.288585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:54.536715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:29.620941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:32.580694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:36.083664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:39.498672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:42.315184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:45.256427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:48.641043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:51.622200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:54.863711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:29.935631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:32.908663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:36.386672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:39.799668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:42.650187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:45.571424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:48.961046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:51.967997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:55.200709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:30.289661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:33.405663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:37.127670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:40.139674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:42.983205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:45.994407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:49.312047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-07T14:11:52.342395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-07T14:12:20.216237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Acc_lengthCarEducation_typeExitedFamily_statusGeographyHousing_typeIDIncome_typeKidsOccupation_typePropertyTargetTotal_incomeUnemployedYears_employedagebalancecr_scorecredit_cardgendertenureterm_deposit
Acc_length1.0000.0440.0050.0190.0400.0160.025-0.0040.019-0.0070.0250.0000.0890.0280.0180.078-0.006-0.000-0.0080.0000.0000.0220.011
Car0.0441.0000.0990.0000.1600.0000.0320.0300.1470.0710.2640.0000.0000.2050.1450.0800.0000.0000.0210.0000.0000.0000.000
Education_type0.0050.0991.0000.0140.0510.0000.0480.0250.0980.0000.1770.0220.0190.1120.1420.0300.0000.0000.0000.0100.0120.0000.000
Exited0.0190.0000.0141.0000.0230.1700.0000.0000.0000.0000.0000.0040.0000.0080.0000.0000.3740.1380.0870.0000.1050.0210.000
Family_status0.0400.1600.0510.0231.0000.0000.0640.0160.1080.0770.0910.0380.0210.0120.2160.0520.0090.0050.0000.0000.0070.0000.018
Geography0.0160.0000.0000.1700.0001.0000.0000.0120.0000.0000.0070.0150.0060.0190.0100.0160.0490.3150.0160.0050.0220.0250.000
Housing_type0.0250.0320.0480.0000.0640.0001.0000.0000.0640.0000.0480.2120.0060.0330.1070.0210.0000.0260.0090.0000.0150.0000.000
ID-0.0040.0300.0250.0000.0160.0120.0001.0000.0260.0190.0000.1520.000-0.0090.035-0.0100.004-0.0110.0020.0170.000-0.0080.050
Income_type0.0190.1470.0980.0000.1080.0000.0640.0261.0000.0690.3760.0860.0000.0950.9940.2220.0000.0000.0000.0060.0000.0000.000
Kids-0.0070.0710.0000.0000.0770.0000.0000.0190.0691.0000.0870.0000.0240.0400.1400.143-0.001-0.0130.0020.0000.0000.0300.017
Occupation_type0.0250.2640.1770.0000.0910.0070.0480.0000.3760.0871.0000.0610.0000.0970.6880.1160.0210.0000.0000.0000.0150.0000.000
Property0.0000.0000.0220.0040.0380.0150.2120.1520.0860.0000.0611.0000.0260.0210.0860.0210.0000.0000.0000.0140.0000.0150.000
Target0.0890.0000.0190.0000.0210.0060.0060.0000.0000.0240.0000.0261.0000.0410.0220.0000.0000.0000.0090.0130.0000.0000.000
Total_income0.0280.2050.1120.0080.0120.0190.033-0.0090.0950.0400.0970.0210.0411.0000.1390.172-0.001-0.0110.0170.0000.000-0.0070.000
Unemployed0.0180.1450.1420.0000.2160.0100.1070.0350.9940.1400.6880.0860.0220.1391.0000.4240.0180.0000.0220.0000.0000.0000.008
Years_employed0.0780.0800.0300.0000.0520.0160.021-0.0100.2220.1430.1160.0210.0000.1720.4241.000-0.011-0.0110.0230.0110.000-0.0060.020
age-0.0060.0000.0000.3740.0090.0490.0000.0040.000-0.0010.0210.0000.000-0.0010.018-0.0111.0000.033-0.0050.0130.025-0.0070.013
balance-0.0000.0000.0000.1380.0050.3150.026-0.0110.000-0.0130.0000.0000.000-0.0110.000-0.0110.0331.0000.0060.0380.006-0.0090.000
cr_score-0.0080.0210.0000.0870.0000.0160.0090.0020.0000.0020.0000.0000.0090.0170.0220.023-0.0050.0061.0000.0000.0000.0030.000
credit_card0.0000.0000.0100.0000.0000.0050.0000.0170.0060.0000.0000.0140.0130.0000.0000.0110.0130.0380.0001.0000.0000.0240.000
gender0.0000.0000.0120.1050.0070.0220.0150.0000.0000.0000.0150.0000.0000.0000.0000.0000.0250.0060.0000.0001.0000.0270.000
tenure0.0220.0000.0000.0210.0000.0250.000-0.0080.0000.0300.0000.0150.000-0.0070.000-0.006-0.007-0.0090.0030.0240.0271.0000.000
term_deposit0.0110.0000.0000.0000.0180.0000.0000.0500.0000.0170.0000.0000.0000.0000.0080.0200.0130.0000.0000.0000.0000.0001.000

Missing values

2024-10-07T14:11:55.715758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-07T14:11:57.169388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDExitedgenderageterm_depositCarPropertyGeographycr_scoretenurebalanceUnemployedKidsAcc_lengthTotal_incomecredit_cardYears_employedIncome_typeEducation_typeFamily_statusHousing_typeOccupation_typeTarget
050088041Female42011Greece61920.000015427500.0112.435574WorkingHigher educationCivil marriageRented apartmentOther1
150088060Female41011Spain608183807.860029112500.003.104787WorkingSecondary / secondary specialMarriedHouse / apartmentSecurity staff0
250088081Female42001Greece5028159660.80004270000.018.353354Commercial associateSecondary / secondary specialSingle / not marriedHouse / apartmentSales staff0
350088120Female39001Greece69910.001020283500.000.000000PensionerHigher educationSeparatedHouse / apartmentOther0
450088150Female43011Spain8502125510.82005270000.012.105450WorkingHigher educationMarriedHouse / apartmentAccountants0
550088191Male44011Spain6458113755.780017135000.013.269061Commercial associateSecondary / secondary specialMarriedHouse / apartmentLaborers0
650088250Male50010Greece82270.000025130500.013.019911WorkingIncomplete higherMarriedHouse / apartmentAccountants1
750088301Female29001Netherlands3764115046.740031157500.014.021985WorkingSecondary / secondary specialMarriedHouse / apartmentLaborers1
850088340Male44001Greece5014142051.070144112500.004.435409WorkingSecondary / secondary specialSingle / not marriedHouse / apartmentOther0
950088360Male27011Greece6842134603.880324270000.013.184186WorkingSecondary / secondary specialMarriedHouse / apartmentLaborers0
IDExitedgenderageterm_depositCarPropertyGeographycr_scoretenurebalanceUnemployedKidsAcc_lengthTotal_incomecredit_cardYears_employedIncome_typeEducation_typeFamily_statusHousing_typeOccupation_typeTarget
969951429730Female23000Greece76320.000118180000.012.535302WorkingSecondary / secondary specialMarriedHouse / apartmentLaborers1
970051435780Female36010Spain5634143680.470014157500.012.628391WorkingIncomplete higherSingle / not marriedWith parentsDrivers1
970151456900Male38001Spain6783124483.531017306000.010.000000PensionerHigher educationMarriedHouse / apartmentOther1
970251457600Female31010Spain644586006.300010135000.0113.235042WorkingHigher educationMarriedHouse / apartmentOther1
970351460780Female43001Netherlands6827111094.050148108000.013.099311WorkingSecondary / secondary specialSingle / not marriedHouse / apartmentSales staff1
970451486940Male22000Greece62590.000020180000.010.542106PensionerSecondary / secondary specialCivil marriageMunicipal apartmentLaborers1
970551490550Male36001Spain73310.000019112500.007.375921Commercial associateSecondary / secondary specialMarriedHouse / apartmentOther1
970651497290Female30011Greece51260.00002190000.014.711938WorkingSecondary / secondary specialMarriedHouse / apartmentOther1
970751498380Female41001Greece6427115171.710032157500.013.627727PensionerHigher educationMarriedHouse / apartmentMedicine staff1
970851503370Female22001Netherlands73710111543.260013112500.003.266323WorkingSecondary / secondary specialSingle / not marriedRented apartmentLaborers1